It seems like the loudest voices in AI often fall into one of two groups. There are the boomers – the techno-optimists – who think that AI is going to bring us into an era of untold prosperity. And then there are the doomers, who think there’s a good chance AI is going to lead to the end of humanity as we know it.

While these two camps are, in many ways, completely at odds with one another, they do share one thing in common: they both buy into the hype of artificial intelligence.

But when you dig deeper into these systems, it becomes apparent that both of these visions – the utopian one and the doomy one – are based on some pretty tenuous assumptions.

Kate Crawford has been trying to understand how AI systems are built for more than a decade. She’s the co-founder of the AI Now institute, a leading AI researcher at Microsoft, and the author of Atlas of AI: Power, Politics and the Planetary Cost of AI.

Crawford was studying AI long before this most recent hype cycle. So I wanted to have her on the show to explain how AI really works. Because even though it can seem like magic, AI actually requires huge amounts of data, cheap labour and energy in order to function. So even if AI doesn’t lead to utopia, or take over the world, it is transforming the planet – by depleting its natural resources, exploiting workers, and sucking up our personal data. And that’s something we need to be paying attention to.

Mentioned:

“ELIZA—A Computer Program For the Study of Natural Language Communication Between Man And Machine” by Joseph Weizenbaum

“Microsoft, OpenAI plan $100 billion data-center project, media report says,” Reuters

“Meta ‘discussed buying publisher Simon & Schuster to train AI’” by Ella Creamer

“Google pauses Gemini AI image generation of people after racial ‘inaccuracies’” by Kelvin Chan And Matt O’brien

“OpenAI and Apple announce partnership,” OpenAI

Fairwork

“New Oxford Report Sheds Light on Labour Malpractices in the Remote Work and AI Booms” by Fairwork

“The Work of Copyright Law in the Age of Generative AI” by Kate Crawford, Jason Schultz

“Generative AI’s environmental costs are soaring – and mostly secret” by Kate Crawford

“Artificial intelligence guzzles billions of liters of water” by Manuel G. Pascual

“S.3732 – Artificial Intelligence Environmental Impacts Act of 2024″

“Assessment of lithium criticality in the global energy transition and addressing policy gaps in transportation” by Peter Greim, A. A. Solomon, Christian Breyer

“Calculating Empires” by Kate Crawford and Vladan Joler

Further Reading:

“Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” by Kate Crawford

“Excavating AI” by Kate Crawford and Trevor Paglen

“Understanding the work of dataset creators” from Knowing Machines

“Should We Treat Data as Labor? Moving beyond ‘Free’” by I. Arrieta-Ibarra et al.